Described are examples for receiving, from one or more second virtual radio access network (vRAN) workloads operating one or more second cells, an indication of a measurement of at least a first signal transmitted by a first vRAN workload operating a first cell, computing, based on measurements of at least the first signal as received from the one or more second vRAN workloads, a boundary of the first cell, and adjusting, based on the boundary of the first cell, a transmit parameter of the first vRAN workload for transmitting signals in the first cell.
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2. The computer-implemented method of claim 1, wherein computing the boundary of the first cell is based on a function of the measurements of the multiple signals as determined by the one or more second vRAN workloads.
This invention relates to wireless communication systems, specifically to methods for determining cell boundaries in virtualized radio access networks (vRAN). The problem addressed is the need for accurate and dynamic boundary computation in vRAN environments where multiple signals are processed by distributed virtual workloads. Traditional methods often rely on static or simplified models, leading to inefficiencies in resource allocation and interference management. The method involves computing the boundary of a wireless communication cell by analyzing measurements of multiple signals received by one or more second vRAN workloads. These workloads are responsible for processing signal data from the network. The boundary computation is based on a function that incorporates these signal measurements, allowing for real-time adjustments to cell boundaries. This dynamic approach improves network performance by optimizing coverage and minimizing interference. The method ensures that the boundary calculation is derived from actual signal data rather than predefined or estimated values, enhancing accuracy. The use of multiple signals and distributed workloads enables a more comprehensive and adaptive boundary determination process. This solution is particularly useful in dense network deployments where precise cell boundary management is critical for efficient spectrum utilization and service quality.
3. The computer-implemented method of claim 1, wherein the indications of the measurements correspond to a computed boundary of the first cell computed by each of the one or more second vRAN workloads based on respective measurements of the multiple signals.
This technical summary describes a method for managing wireless network coverage in a virtualized radio access network (vRAN) environment. The method addresses the challenge of accurately determining cell boundaries in a dynamic network where multiple virtualized workloads handle different aspects of radio access. Each of the one or more second vRAN workloads processes multiple signals from the network to compute a boundary for a first cell. These workloads generate indications of measurements, which correspond to the computed boundaries. The computed boundaries are derived from the respective measurements of the signals, allowing the system to dynamically adjust coverage areas based on real-time data. This approach enables precise and adaptive cell boundary management, improving network performance and reliability in virtualized environments. The method leverages distributed processing across multiple workloads to enhance accuracy and responsiveness in determining cell coverage.
4. The computer-implemented method of claim 3, wherein computing the boundary is based on a function of the computed boundary received from each of the one or more second vRAN workloads.
This invention relates to virtualized radio access network (vRAN) systems, specifically addressing the challenge of dynamically determining and managing boundaries between different vRAN workloads to optimize resource allocation and performance. In a vRAN architecture, multiple workloads may operate concurrently, each handling different aspects of radio access network functions. The invention provides a method for computing and adjusting these boundaries in real-time to ensure efficient utilization of shared resources, such as compute, storage, and network bandwidth, while maintaining service quality. The method involves receiving boundary computations from one or more second vRAN workloads, where each workload contributes to the determination of the boundary. The boundary is then computed as a function of these received boundary values, allowing for a collaborative and adaptive approach to resource partitioning. This ensures that the boundaries between workloads are dynamically adjusted based on current operational conditions, workload demands, and system constraints. The solution enables flexible and efficient resource management in virtualized environments, improving scalability and performance in vRAN deployments. The invention is particularly useful in scenarios where multiple vRAN workloads must coexist and share infrastructure while maintaining optimal performance and reliability.
5. The computer-implemented method of claim 1, wherein adjusting the transmit parameter includes using machine learning to compare the measurements of the multiple signals based on a trained model of signal measurements that resulted in adjusting transmit parameters of other cells.
This invention relates to wireless communication systems, specifically optimizing transmit parameters in cellular networks to improve signal quality and reduce interference. The problem addressed is the dynamic adjustment of transmit parameters in response to varying signal conditions, which is traditionally done manually or through rule-based algorithms that lack adaptability. The method involves measuring multiple signals in a cellular network, such as signal strength, interference levels, or quality metrics, to assess performance. A machine learning model, trained on historical data of signal measurements and corresponding transmit parameter adjustments from other cells, is used to analyze these measurements. The model predicts the optimal transmit parameters, such as power levels, beamforming weights, or frequency allocations, to adjust in the current cell. This approach leverages learned patterns from past adjustments to improve decision-making, enhancing network efficiency and user experience. The machine learning model is trained using data from multiple cells, allowing it to generalize and adapt to different network conditions. By comparing current measurements to the trained model, the system dynamically adjusts transmit parameters to mitigate interference, improve coverage, or optimize capacity. This method automates and optimizes parameter tuning, reducing manual intervention and improving network performance.
6. The computer-implemented method of claim 1, wherein the transmit parameter is at least one of a transmit power or a modulation for the first vRAN workload to use in providing the first cell.
This invention relates to virtualized radio access network (vRAN) systems, specifically optimizing transmit parameters for workloads managing wireless cells. The problem addressed is inefficient resource allocation in vRAN environments, where workloads may not dynamically adjust transmit power or modulation schemes to optimize performance and spectrum utilization. The method involves dynamically configuring transmit parameters for a first vRAN workload responsible for providing a first wireless cell. These parameters include transmit power or modulation schemes, which are adjusted based on real-time conditions such as network load, interference levels, or quality of service requirements. By optimizing these parameters, the system improves spectral efficiency, reduces interference, and enhances overall network performance. The solution ensures that the vRAN workload can adapt its transmission characteristics to varying operational demands, leading to better resource utilization and user experience. The approach may also involve coordinating parameter adjustments across multiple workloads to maintain network-wide efficiency. This dynamic adaptation is particularly valuable in dense or heterogeneous network deployments where static configurations would lead to suboptimal performance.
7. The computer-implemented method of claim 1, wherein the first VRAN workload and the one or more second vRAN workloads provide a centralized unit (CU), distributed unit (DU), or remote unit (RU) functionality in a fifth generation (5G) mobile edge computing (MEC) environment.
This technical summary describes a computer-implemented method for managing virtualized radio access network (vRAN) workloads in a fifth-generation (5G) mobile edge computing (MEC) environment. The method addresses the challenge of efficiently distributing and managing vRAN workloads to optimize network performance and resource utilization in 5G networks. The method involves deploying a first vRAN workload and one or more second vRAN workloads, where these workloads provide specific functionalities in a 5G MEC environment. The workloads can function as a centralized unit (CU), distributed unit (DU), or remote unit (RU). The CU handles higher-layer protocols and control functions, the DU manages lower-layer protocols and scheduling, and the RU performs radio frequency (RF) processing and signal transmission. By virtualizing these components, the method enables flexible and scalable deployment of vRAN functions in edge computing environments, reducing latency and improving network efficiency. The method ensures that the vRAN workloads are dynamically allocated and managed based on network demands, optimizing resource usage and enhancing service delivery in 5G networks. This approach supports the deployment of virtualized network functions (VNFs) in MEC environments, enabling faster processing and lower latency for end-users. The solution is particularly useful for operators seeking to modernize their RAN infrastructure while leveraging the benefits of cloud-native and edge computing technologies.
9. The device of claim 8, wherein the at least one processor is configured to compute the boundary of the first cell based on a function of the measurements of the multiple signals as determined by the one or more second vRAN workloads.
This invention relates to wireless communication systems, specifically to virtualized radio access network (vRAN) architectures that process signals from multiple cells to optimize network performance. The problem addressed is the need for accurate and efficient boundary determination of wireless cells to improve coverage, capacity, and handover reliability in dynamic network environments. The device includes at least one processor configured to compute the boundary of a first cell based on measurements of multiple signals received from one or more second vRAN workloads. These workloads are responsible for processing signals from other cells in the network. The boundary computation is performed using a function that incorporates these signal measurements, allowing the system to dynamically adjust cell boundaries in response to real-time network conditions. This approach enhances interference management, load balancing, and seamless user mobility across cells. The device may also include additional components such as antennas, transceivers, and memory to support signal processing and data storage. The system leverages virtualization to distribute workloads across multiple processing units, improving scalability and flexibility in network deployment. The boundary computation function may involve statistical analysis, machine learning, or other computational techniques to derive optimal cell edges based on signal quality, interference patterns, and user distribution. This solution enables adaptive network optimization without requiring manual configuration, reducing operational complexity and improving overall network efficiency.
10. The device of claim 8, wherein the indications of the measurements correspond to a computed boundary of the first cell computed by each of the one or more second vRAN workloads based on respective measurements of the multiple signals.
This invention relates to virtualized radio access network (vRAN) systems, specifically addressing the challenge of accurately determining cell boundaries in wireless networks. The system includes a first vRAN workload that manages a first cell and one or more second vRAN workloads that measure multiple signals from the first cell. These second workloads compute a boundary of the first cell based on their respective measurements. The computed boundaries are then used to generate indications, which can be utilized for network optimization, interference management, or handover decisions. The system may also include a controller that processes these indications to refine cell coverage or adjust network parameters dynamically. The measurements may involve signal strength, quality, or other radio metrics, and the computed boundaries help ensure precise cell coverage mapping. This approach improves network efficiency by enabling real-time adjustments based on dynamic signal conditions, reducing interference and enhancing user experience. The invention is particularly useful in dense urban environments where cell boundaries frequently overlap or shift due to environmental factors.
11. The device of claim 10, wherein the at least one processor is configured to compute the boundary based on a function of the computed boundary received from each of the one or more second vRAN workloads.
A system for managing virtualized radio access network (vRAN) workloads includes a central processing unit (CPU) and at least one processor configured to compute a boundary for distributing workloads among one or more second vRAN workloads. The boundary is determined based on a function of the computed boundaries received from each of the second vRAN workloads. The system dynamically adjusts workload distribution to optimize performance and resource utilization. The second vRAN workloads may include virtualized baseband processing units (vBBUs) or other network functions, and the boundary computation ensures efficient allocation of tasks across these workloads. The system may also include a memory for storing configuration data and a communication interface for exchanging data with the vRAN workloads. The boundary function may incorporate factors such as load balancing, latency, or processing capacity to determine optimal workload distribution. This approach improves scalability and reliability in virtualized network environments by dynamically adapting to changing network conditions.
12. The device of claim 8, wherein the at least one processor is configured to adjust the transmit parameter at least in part by using machine learning to compare the measurements of the multiple signals based on a trained model of signal measurements that resulted in adjusting transmit parameters of other cells.
This invention relates to wireless communication systems, specifically to optimizing transmit parameters in cellular networks to improve signal quality and reduce interference. The problem addressed is the dynamic adjustment of transmit parameters in a cell to enhance performance based on real-time signal measurements and historical data from other cells. The device includes at least one processor configured to measure multiple signals in a cell, such as signal strength, interference levels, or quality metrics. The processor analyzes these measurements to determine optimal transmit parameters, such as power levels, beamforming settings, or frequency allocations. A key feature is the use of machine learning to adjust these parameters by comparing current measurements with a trained model. The model is based on historical signal measurements from other cells where transmit parameters were previously adjusted, allowing the system to predict effective adjustments for the current cell. This approach leverages collective data to improve individual cell performance, reducing manual configuration and enhancing network efficiency. The solution aims to dynamically adapt to changing conditions, minimizing interference and optimizing resource allocation across the network.
13. The device of claim 8, wherein the transmit parameter is at least one of a transmit power or a modulation for the first vRAN workload to use in providing the first cell.
A wireless communication system includes a virtualized radio access network (vRAN) architecture where radio access network (RAN) functions are decoupled from physical hardware and run as virtualized workloads. This architecture allows for flexible deployment and resource sharing but introduces challenges in dynamically adjusting transmit parameters to optimize performance. The invention addresses this by providing a device that dynamically configures transmit parameters for vRAN workloads to improve efficiency and coverage. The device includes a controller that monitors network conditions and workload performance, then adjusts transmit parameters such as transmit power or modulation schemes for individual vRAN workloads. These adjustments are made based on factors like signal quality, interference levels, and resource availability. For example, the device may increase transmit power for a vRAN workload serving a cell with weak signal conditions or switch to a more robust modulation scheme to enhance reliability. The dynamic configuration ensures optimal use of network resources while maintaining service quality. This approach is particularly useful in dense urban environments or high-traffic scenarios where static configurations would lead to inefficiencies. The invention improves network performance by adapting to real-time conditions without manual intervention.
14. The device of claim 8, wherein the first VRAN workload and the one or more second vRAN workloads provide a centralized unit (CU), distributed unit (DU), or remote unit (RU) functionality in a fifth generation (5G) mobile edge computing (MEC) environment.
This invention relates to virtualized radio access network (vRAN) workloads in a fifth-generation (5G) mobile edge computing (MEC) environment. The problem addressed is the efficient deployment and management of vRAN functions in a distributed computing architecture to optimize performance, latency, and resource utilization in 5G networks. The device includes a computing platform configured to host multiple virtualized workloads, including a first vRAN workload and one or more second vRAN workloads. These workloads are designed to provide different functional roles in a 5G network, such as centralized unit (CU), distributed unit (DU), or remote unit (RU) functionality. The CU handles higher-layer protocols and centralized control, the DU manages real-time radio scheduling and medium access control, and the RU performs physical-layer radio functions. By virtualizing these components, the system enables flexible deployment, scalability, and dynamic resource allocation in a MEC environment, where computing resources are located close to the network edge to reduce latency and improve user experience. The device may also include a workload manager to orchestrate the allocation and execution of these vRAN workloads across available computing resources, ensuring efficient utilization and minimizing interference between different network functions. This approach supports seamless integration with existing 5G infrastructure while enhancing network agility and performance.
16. The computer-readable medium of claim 15, wherein the code for computing computes the boundary of the first cell based on a function of the measurements of the multiple signals as determined by the one or more second vRAN workloads.
This invention relates to wireless communication systems, specifically to methods for optimizing cell boundary determination in virtualized radio access networks (vRAN). The problem addressed is the need for dynamic and accurate cell boundary computation in vRAN environments where multiple signals and workloads interact. Traditional methods often rely on static or simplified models, leading to inefficiencies in resource allocation and signal quality. The invention involves a computer-readable medium storing code that, when executed, performs operations for computing cell boundaries in a vRAN. The system includes one or more first vRAN workloads that generate measurements of multiple signals, such as signal strength, interference levels, or other radio metrics. These measurements are processed by one or more second vRAN workloads to determine the boundary of a first cell. The boundary computation is based on a function of the signal measurements, allowing for adaptive adjustments to the cell's coverage area. This dynamic approach improves network performance by optimizing signal coverage and reducing interference. The invention further includes mechanisms for handling multiple cells, where the boundary of the first cell is computed in relation to other cells, ensuring coordinated and efficient network operation. The use of virtualized workloads enables flexible and scalable deployment, adapting to changing network conditions and user demands. This solution enhances the reliability and efficiency of vRAN systems by providing a data-driven method for cell boundary determination.
17. The computer-readable medium of claim 15, wherein the indications of the measurements correspond to a computed boundary of the first cell computed by each of the one or more second vRAN workloads based on respective measurements of the multiple signals, and wherein the code for computing computes the boundary based on a function of the computed boundary received from each of the one or more second vRAN workloads.
This invention relates to virtualized radio access network (vRAN) systems, specifically methods for determining cell boundaries in a distributed computing environment. The problem addressed involves accurately defining the coverage area of a cell in a vRAN system where multiple virtualized workloads process signal measurements from user devices. Traditional approaches may struggle with consistency and accuracy when multiple workloads independently compute boundaries based on the same measurements, leading to potential overlaps or gaps in coverage. The invention provides a solution where multiple vRAN workloads independently measure signals from user devices within a cell and compute preliminary boundaries for that cell. These preliminary boundaries are then aggregated and processed by a central computing function, which computes a final boundary based on a mathematical function of the individual boundaries. This function may involve averaging, weighted averaging, or other statistical methods to ensure a consistent and optimized cell boundary. The system improves coverage accuracy by leveraging distributed processing while mitigating inconsistencies that arise from independent boundary computations. This approach is particularly useful in dynamic environments where signal conditions and user device locations change frequently, requiring real-time adjustments to cell boundaries. The invention enhances network performance by ensuring seamless handover and minimizing coverage gaps.
18. The computer-readable medium of claim 15, wherein the code for adjusting adjusts the transmit parameter at least in part by using machine learning to compare the measurements of the multiple signals based on a trained model of signal measurements that resulted in adjusting transmit parameters of other cells.
This invention relates to wireless communication systems, specifically optimizing transmit parameters in cellular networks to improve signal quality and reduce interference. The problem addressed is the need for dynamic adjustment of transmit parameters, such as power or beamforming, to adapt to changing environmental conditions and user demands while minimizing interference with neighboring cells. The invention involves a system that measures multiple signals in a wireless network, including signals from neighboring cells, and adjusts transmit parameters based on these measurements. The adjustment process uses machine learning, specifically a trained model, to analyze historical signal measurement data from other cells where transmit parameters were previously adjusted. The model predicts optimal adjustments for the current cell by comparing the current measurements with past data where similar conditions led to successful parameter changes. This approach improves efficiency by leveraging learned patterns rather than relying solely on real-time calculations. The system may also include additional features, such as selecting specific signals for measurement, filtering measurements to exclude outliers, and dynamically adjusting the frequency or timing of measurements to balance accuracy and resource usage. The machine learning model is trained on historical data to identify correlations between signal measurements and effective parameter adjustments, enabling more accurate and adaptive decisions. This method enhances network performance by reducing interference and optimizing signal quality across multiple cells.
19. The computer-readable medium of claim 15, wherein the transmit parameter is at least one of a transmit power or a modulation for the first vRAN workload to use in providing the first cell.
This invention relates to virtualized radio access network (vRAN) systems, specifically addressing the dynamic allocation of transmit parameters such as power and modulation schemes to optimize performance in a shared infrastructure. The problem solved is the inefficient use of radio resources in vRAN environments where multiple workloads compete for limited transmission capabilities, leading to suboptimal coverage, capacity, or energy efficiency. The invention involves a system that dynamically adjusts transmit parameters for a first vRAN workload responsible for providing a first cell. The transmit parameters include at least one of transmit power or modulation scheme, which are selected based on real-time conditions such as network load, interference levels, or quality of service requirements. The system ensures that the first vRAN workload can adapt its transmission characteristics to improve spectral efficiency, reduce interference, or conserve energy without requiring manual configuration. The solution leverages a computer-readable medium storing instructions that, when executed, enable the dynamic adjustment of these parameters. This allows the vRAN workload to optimize its radio resource usage while maintaining service quality. The system may also coordinate with other vRAN workloads to avoid conflicts and ensure fair resource allocation across multiple cells. The approach is particularly useful in dense network deployments where efficient spectrum utilization is critical.
20. The computer-readable medium of claim 15, wherein the first VRAN workload and the one or more second vRAN workloads provide a centralized unit (CU), distributed unit (DU), or remote unit (RU) functionality in a fifth generation (5G) mobile edge computing (MEC) environment.
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May 18, 2021
April 9, 2024
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